Biomarkers for pancreatic cancer

ABSTRACT

The present invention relates to pancreatic cancers and the use of biomarkers in biological samples for the diagnosis of such conditions, in particular pancreatic ductal adenocarcinoma. The biomarker panel comprises LYVE1, REG1 and TFF1. Methods of treatment arc also provided, as well as kits useful in the diagnosis and treatment of pancreatic ductal adenocarcinoma. The present invention is particularly useful in detecting early-stage PDAC.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a National Phase of International Application No.PCT/GB2016/050277 filed Feb. 5, 2016, currently pending, whichdesignated the U.S. and that International Application was publishedunder PCT Article 21(2) in English. This application also includes aclaim of priority under 35 U.S.C. § 119(a) and § 365(b) to Britishpatent application No. GB 1501930.0 filed Feb. 5, 2015, the entirety ofwhich is herein incorporated by reference.

The present invention relates to pancreatic cancers and the use ofbiomarkers in biological samples for the diagnosis of such conditions,in particular pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is the most common exocrinepancreatic malignancy accounting for more than 80% of malignantneoplasms arising in pancreas. It is the fourth most common cause ofcancer-related deaths in the Western world. When diagnosed, the majorityof patients display locally advanced disease or have establishedmetastases, therefore surgery is possible in only 10-20% of patients(Sener et al. (1999) J Am Coll Surg, 189:1-7). While the overall 5-yearsurvival rate is less than 5%, the 5-year survival in patients aftersurgical resection and adjuvant chemotherapy can reach 30%.

Pancreatic ductal adenocarcinoma (PDAC) is one of the rare cancers forwhich no significant improvements in the diagnostic and therapeuticapproaches have been made in the last decades. Despite considerableprogress in our understanding of the disease at the molecular level,novel findings have not yet reached the clinic, and the majority ofpatients are still faced with a grim average survival of 5 to 6 months.With over 38,000 PDAC-related deaths in the US and over 40,000 in Europein 2013, this malignancy is currently the fourth leading cause ofcancer-related death, but predicted to become the second by 2030.

PDAC is one of the most challenging cancers to detect. Theretroperitoneal position of the pancreas, a number of complex underlyingmolecular abnormalities, and the lack of specific clinical symptomsresult in a majority of patients presenting at an advanced stage. Fewerthan 20% of patients can thus undergo potentially curative surgery,while the remaining ones can only be offered palliative treatment.

These worrying figures would change significantly with improved tool(s)for early detection, as 5-year survival approaching 70% has beenreported after incidental diagnosis of stage I PDAC tumours, when theywere still confined to the pancreas with a size <2 cm. Importantly, aconsiderable ‘window’ of opportunity of around a decade exists forearlier diagnosis (Yachida S, et al. (2010) Nature 467(7319):1114-1117).Detection at an early stage is also crucial given the poor efficacy ofcurrent therapies for metastatic disease, when potentially curativesurgery is no longer feasible.

Timely detection of PDAC is, however, hampered by several factors: lackof specific clinical symptoms in the early stage of the disease,insufficient sensitivity of current imaging modalities and, despiteintensive efforts, lack of accurate body fluid-based biomarkers ofearly-stage disease (for a review see Kaur et al. (2012) Biomark Med6(5):597-612.). Early stage PDAC is also difficult to differentiate fromchronic pancreatitis (CP), a benign inflammatory disease of the pancreasand one of the risk factors for PDAC. Serum carbohydrate antigen 19.9(CA19.9), the only PDAC biomarker in widespread clinical use at present,suffers from false negative results in patients with Lewis-negativegenotype, poor positive predictive value in the asymptomatic populationand low sensitivity (79%-81%) in symptomatic patients. Less than 50% ofcases with early disease (tumour <2 cm) have raised CA19-9 levels, yetCA19.9 levels may be elevated in various other benign and malignantpancreatic and hepato-biliary diseases (including chronic pancreatitis),as well as in unrelated cystic and inflammatory diseases (for review seeBallehaninna UK, Chamberlain R S, “Serum CA 19-9 as a Biomarker forPancreatic Cancer-A Comprehensive Review”, Indian J Surg Oncol, 2011;2:88-100). In addition, Lewis a/b antigen (which Ca19.9 recognizes) isnot expressed in around 10% of population

Proteomic techniques have recently been used to study protein expressionin pancreatic cancer tissue, pancreatic juice and serum/plasma specimens(see, for example, Koomen et al. (2005) Clin Cancer Res, 11:1110-1118),but none of these have, as yet, resulted in the discovery of biomarkerssuitable for clinical practice.

Recently, urine was studied as a potential source of biomarkers as it isan easily and non-invasively obtained bio-fluid (Pieper et al. (2004)Proteomics, 4:1159-1174). In comparison with plasma, urine proteins areless complex and more thermostable. Furthermore, most common proteins(albumin, uromodulin) comprise a lesser proportion of the urinaryproteome, so sample processing requires less pre-cleaning/fractionation.Approximately 49% of urinary proteins are soluble products of glomerularfiltration of plasma (Barrat et al. (2007) Cmaj, 177:361-368), andtherefore a substantial number of proteins in urine arise fromextrarenal sources (Thongboonkerd et al. (2005) Curr Opin NephrolHypertens, 14:133-139).

In addition to urological cancers, several cancer-related proteins havebeen identified in the urine of patients with lung, ovarian and breastcancers.

WO2004/102189 describes biomarkers for the diagnosis pancreatic cancer.Serum samples from patients with pancreatic cancer were compared withserum samples from healthy donors and the resulting biomarkerscharacterized by their weight. A similar approach was carried out inWO2004/099432, which provides further biomarkers for detectingpancreatic cancer. WO2000/34787 describes methods of diagnosingepithelial cancers involving the measurement of the levels of biomarkersin urine

There are currently no specific and sensitive biomarkers for earlydiagnosis of pancreatic adenocarcinoma. This is extremely important, asif pancreatic adenocarcinoma can be detected early (e.g. stage I), thesurvival of such patients can be greatly improved (currently, mostpatients are diagnosed at stage III/IV with a 5-year survival rate of<5%). Highly accurate biomarkers for early detection are thus expectedto significantly impact on a patient's prognosis.

There is therefore a need for a sensitive and specific panel of markersthat would enable not only early diagnosis of PDAC, but also aid indifferentiating between PDAC and other tumours, as well as between PDACand chronic pancreatitis (CP). Preferably the markers will be detectablein a sample that is easy and non-invasive to obtain and is sensitiveenough to detect the disease during its early stages.

SUMMARY OF THE INVENTION

In a first aspect of the invention there is provided a biomarker paneluseful in the diagnosis of pancreatic ductal adenocarcinoma (PDAC), thepanel comprising LYVE1, REG1 and TFF1. References to REG1 herein includeREG1A and REG1B. In some embodiments, the panel may further compriseCA19.9.

In a second aspect of the invention there is provided a method ofscreening or testing for pancreatic ductal adenocarcinoma (PDAC)comprising detecting the level of expression or concentration of aprotein selected from the group consisting of LYVE1, REG1 (for exampleREG1A and/or REG1B) and TFF1, or combinations thereof, in a biologicalsample. In some embodiments, the biological sample is a urine sample.

In a third aspect of the invention there is provided a protein selectedfrom the group consisting of LYVE1, REG1 (for example REG1A and/orREG1B) and TFF1, or a combination thereof, for use in diagnosingpancreatic ductal adenocarcinoma. There is also provided the use of aprotein selected from the group consisting of LYVE1, REG1 (for exampleREG1A and/or REG1B) and TFF1 in methods of detecting or diagnosing PDAC.

In a fourth aspect of the invention there is provided a kit for testingfor pancreatic ductal adenocarcinoma comprising a means for detectingthe level of expression or concentration of LYVE1, REG1 (for exampleREG1A and/or REG1B) or TFF1, or combinations thereof, in a biologicalsample.

In a further aspect of the invention there is provided a method ofdistinguishing between PDAC and chronic pancreatitis, comprisingdetecting the level of expression of LYVE1, REG1 (for example REG1Aand/or REG1B) and/or TFF1 in a urine sample. The method may comprisecomparing the expression levels of each of the proteins with areference.

In a further aspect of the invention there is provided a method ofdetecting or diagnosing early stage PDAC, for example stage I or stageII PDAC, comprising detecting the level of expression or concentrationof the biomarker panel proteins LYVE1, REG1 (REG1A and/or REG1B) and/orTFF1 in a biological sample. The method may further comprise comparingthe expression levels or concentration of each of the quantifiedproteins with a reference.

In a still further aspect of the invention there is provided a method oftreating PDAC in a patient, comprising detecting the level of expressionor concentration of a protein selected from the group consisting ofLYVE1, REG1 (for example REG1A and/or REG1B) and TFF1, or combinationsthereof, in a biological sample, optionally comparing the level ofexpression with a control/reference, and proceeding with treatment forPDAC if PDAC is diagnosed or suspected. Methods of prognosis are alsoincluded in the present invention, comprising determining the level ofexpression or concentration of one or more proteins selected from thegroup consisting of LYVE1, REG1 (for example REG1A and/or REG1B) andTFF1 in a biological sample, optionally comparing the level ofexpression with a control/reference, and determining the prognosis forthe patient.

In embodiments of the invention, the biomarkers used in the inventioncan be used separately (i.e. only one of LYVE1, REG1 (for example REG1Aand/or REG1B) and TFF1), they can be used in pairs (e.g. LYVE1 and REG1(for example REG1A and/or REG1B), LYVE1 and TFF1, or REG1 (for exampleREG1A and/or REG1B) and TFF1), three biomarkers might be used together(each of LYVE1 and TFF1 and one of REG1A or REG1B), or all four may beused (LYVE1, TFF1, REG1A and REG1B). A fifth biomarker, CA19.9 may alsobe used. In embodiments of the invention, the level of expression ofLYVE1, REG1 (for example REG1A and/or REG1B) and/or TFF1 may bedetermined by quantifying gene expression (for example quantifying mRNAin a biological sample), or by quantifying protein expression (forexample quantifying protein concentration in a biological sample).

BRIEF DESCRIPTION OF THE FIGURES

Reference is made to a number of Figures as follows:

FIG. 1: Urine concentration of the candidate protein biomarkers. A,Scatter dot plots of LYVE1, REG1A, REG1B and TFF1 protein concentration(creatinine-normalised) analyzed by ELISA in healthy, chronicpancreatitis (CP) and pancreatic adenocarcinoma (PDAC) patients' urine.Upper bars: Kruskal-Wallis/Dunn's post test, ***: P<0.001; B,Statistical summary, median and Interquartile range (IQR) ofraw/creatinine-normalised data for the biomarkers, median and IQR ofurine creatinine (mmol/L), as well as plasma CA19.9 by sample groups areshown.

FIG. 2—Diagnostic performance of urine biomarkers in discriminatingpancreatic adenocarcinoma patients from healthy controls. A, ROC curvesof PDAC (n=143) versus healthy (n=59) subjects for individualcreatinine-normalised urine biomarkers in the training set (70% of thedata); B, ROC curves of PDAC versus healthy for the panel in thetraining set and in the independent validation set (30% of the data:PDAC n=49, healthy n=28); C, Summary table. AUC: area under the curve,SN: sensitivity, SP: specificity, with corresponding 95% ConfidenceIntervals (CI). SN and SP in the validation set are derived for optimalcut point determined in the training dataset. cnorm:creatinine-normalised, creat: creatinine.

FIG. 3—Urine concentration of the three biomarkers in different stagesof pancreatic adenocarcinoma. Scatter dot plots of urine LYVE1, REG1A,TFF1 protein concentration (creatinine-normalised) in urines of healthy(n=87) and pancreatic adenocarcinoma patients at different stages ofdisease development (I-IIA n=16, I-II n=71, III-IV n=77). Bars indicatemedian and IQR values. Upper bars: Kruskal-Wallis/Dunn's post test, *:P<0.05, **: P<0.01, ***: P<0.001.

FIG. 4—Diagnostic performance of urine biomarkers in discriminatingearly pancreatic adenocarcinoma patients form healthy individuals. A,ROC curves of stages I-II PDAC (n=56) versus healthy (n=61) subjects forindividual urine biomarkers in the training set (70% of the data); B,ROC curves of stage I-II PDAC versus healthy for the panel in thetraining set and in the independent validation set (30% of the data;PDAC n=15, healthy n=26); C, Summary table. AUC: area under the curve,SN: sensitivity, SP: specificity, with corresponding 95% ConfidenceIntervals (CI). SN and SP in the validation set are derived for optimalcutpoint determined in the training dataset. cnorm:creatinine-normalised, creat: creatinine.

FIG. 5—Diagnostic performance of the urine biomarker panel and CA19.9 indiscriminating early pancreatic adenocarcinoma patients form healthyindividuals. A, ROC curves of the biomarker panel with correspondingplasma CA19.9 alone and in combination comparing healthy urine (n=28),and urines from PDAC stages I-II, n=71 and I-IIA, n=16 (B). C, Summarytable. AUC: area under the curve, SN: sensitivity, SP: specificity with95% Confidence Interval (CI). SN and SP in the validation set werederived for optimal cutpoint determined in the training dataset.

Legend for FIG. 5C

{circumflex over ( )} Optimal cutpoint for CA19.9 is 37 U/mL

+ DeLong's 1-sided test for correlated/paired AUCs to assess whether theurine panel gives a significantly greater AUC compared to plasma CA19.9alone used as a dichotomous biomarker (0.973 versus 0.880), p=0.005

$ DeLong's 1-sided test for correlated/paired AUCs to assess whether theaddition of plasma CA19.9 used as a dichotomous biomarker

significantly increase the AUC over the urine panel alone (0.991 versus0.973), p=0.04

++ DeLong's 1-sided test for correlated/paired AUCs to assess whetherthe urine panel gives a significantly greater AUC compared to plasmaCA19.9 alone used as a dichotomous biomarker (0.971 versus 0.839),p=0.006

$$ DeLong's 1-sided test for correlated/paired AUCs to assess whetherthe addition of plasma CA19.9 used as a dichotomous biomarkersignificantly increase the AUC over the urine panel alone (0.969 versus0.971), p=0.7

FIG. 6—Urine proteome analysis. A, schematic outline of the study; B,classification of total identified proteins according to sub-cellularlocalisation; and C, functional activity determined by Ingenuity PathwayAnalysis. H: healthy, CP: chronic pancreatitis, PDAC: pancreatic ductaladenocarcinoma, GeLC/MS/MS: SDS-PAGE-Liquid Chromatography-Tandem MassSpectrometry.

FIG. 7—Correlation of the three urinary biomarkers and plasma CA19.9(CA19.9p). A, Correlation plots (Navy blue/darkest: Healthy;Turquoise/lightest: chronic pancreatitis (CP); Purple/intermediate:pancreatic adenocarcinoma (PDAC). B, Pearson correlation coefficientsand corresponding significance (NS: non-significant, *: P<0.05, **:P<0.01, ***: P<0.001).

FIG. 8—Diagnostic performance of urine biomarkers in discriminatingpancreatic adenocarcinoma all stages (A-C) and stage I-II (D-F) fromchronic pancreatitis patients. A, ROC curves of PDAC (n=143) versus CP(n=62) patients for individual urine biomarkers in the training set (70%of the data). B, ROC curves of PDAC versus CP patients for the panel inthe training set and in the independent validation set (30% of the data,PDAC n=49, CP n=30). C, Summary table. D, ROC curves of individual urinebiomarkers in training dataset (70%, PDAC n=56, CP=66). E, ROC curves ofthe panel in training and validation (PDAC n=15, CP n=26) dataset. F,Summary table. Cnorm, creatinine-normalised, creat, creatinine, AUC:area under the curve SN: sensitivity, SP: specificity. with 95%Confidence Interval (CI). SN and SP in the validation set were derivedfor optimal cutpoint determined in the training dataset.

FIG. 9—Exploratory comparison of plasma CA19.9 and the urine biomarkerpanel in discriminating early pancreatic adenocarcinoma from chronicpancreatitis patients. A, ROC curves of the biomarker panel withcorresponding plasma CA19.9 alone and in combination comparing CP urine(n=50), and urines from PDAC stages I-II (n=71) and I-IIA (n=16) (B). C,Summary table. AUC: area under the curve, SN: sensitivity, SP:specificity with 95% Confidence

Interval (CI). SN and SP in the validation set were derived for optimalcutpoint determined in the training dataset.

Legend for FIG. 9C:

{circumflex over ( )} Optimal cutpoint for CA19.9 is 37 U/mL

+ DeLong's 1-sided test for correlated/paired AUCs to assess whether theurine panel gives a significantly greater AUC compared to plasma CA19.9alone used as a dichotomous biomarker (0.830 versus 0.775), p=0.1

$ DeLong's 1-sided test for correlated/paired AUCs to assess whether theaddition of plasma CA19.9 used as a dichotomous biomarker

significantly increase the AUC over the urine panel alone (0.885 versus0.830), p=0.01

++ DeLong's 1-sided test for correlated/paired AUCs to assess whetherthe urine panel gives a significantly greater AUC compared to plasmaCA19.9 alone used as a dichotomous biomarker (0.871 versus 0.735),p=0.004

$$ DeLong's 1-sided test for correlated/paired AUCs to assess whetherthe addition of plasma CA19.9 used as a dichotomous biomarkersignificantly increase the AUC over the urine panel alone (0.866 versus0.871), p=0.6

FIG. 10—Urine biomarker concentrations in different tumours. A,Demographic details. B, Scatter dot plots of urine LYVE1, REG1A andplasma CA19.9 in different hepatobiliary pathologies and early stages ofpancreatic adenocarcinoma (I-IIA, n=16) and I-II (n=71). The level ofTFF1 protein was not measured in these samples due to substantialmodifications made to the original ELISA assay by the source company atthe moment of this analysis. IPMN (n=33): intraductal papillary mucinousneoplasm, AMP (n=26): ampullary cancer, NET (n=18): neuroendocrinetumour, CHL (n=24): cholangiocarcinoma, DuCA (n=16): duodenal cancer.Bars indicate median and IQR values. Upper bars: Kruskal-Wallis/Dunn'spost test, *: P<0.5, **: P<0.01, ***: P<0.001; where not shown,difference not statistically significant.

FIG. 11—Expression of the biomarker panel proteins in pancreatic cancertissues. A, Immunohistochemical analysis of REG1A: i) REG1A in poorlydifferentiated PDAC, ii) luminal REG1A in malignant glands. B, TFF1: i)heterogenous expression in cancer, ii) luminal TFF1 in malignant gland.C, LYVE1 expression in the scattered lymphatic vessels i) in the musclelayer and ii) in the stroma surrounding malignant gland. D, Thebiomarker levels during monitoring of pancreatic adenocarcinomapatients: LYVE1, REG1A and TFF1 were measured using ELISA in urinesamples collected before surgery and during the patients' follow up.Each point represents log-transformed ELISA values at a particular timepoint (x-axis).

DETAILED DESCRIPTION OF THE INVENTION

Biomarker Panels

The present invention provides a biomarker panel useful in the diagnosisof pancreatic ductal adenocarcinoma (PDAC), the panel comprising LYVE1,REG1 (REG1A and/or REG1B) and TFF1. In particular, the present inventionprovides a method of diagnosing, screening or testing for pancreaticductal adenocarcinoma (PDAC) comprising detecting or level of expressionof a gene or protein selected from the group consisting of LYVE1, REG1(REG1A and/or REG1B) and TFF1, or combinations thereof, in a biologicalsample. In preferred embodiments, at least two of LYVEI, REG1 and TFF1are used. In a more preferred embodiment, all three are used.

LYVE1 is also known as lymphatic vessel endothelial hyaluronan receptorand extracellular link domain containing 1 (XLKD1) is a type I integralmembrane glycoprotein. The encoded protein acts as a receptor and bindsto both soluble and immobilized hyaluronan. References to LYVE includeNCBI (GenBank) reference sequence transcript NM_006691.3 (GI:15130120),NCBI protein ID NP_006682.2 (GI:40549451), GeneID:10894, and HGNC (HUGO(Human Gene Organisation) Gene Nomenclature Committee gene ID):14687.The gene has 4 splice variants; two are protein coding (full lengthprotein has 322 amino acids, and the second one 218 amino acids). Thefull sequence of the protein is as follows:

(SEQ ID NO: 1) MARCFSLVLLLTSIWTTRLLVQGSLRAEELSIQVSCRIMGITLVSKKANQQLNFTEAKEACRLLGLSLAGKDQVETALKASFETCSYGWVGDGFVVISRISPNPKCGKNGVGVLIWKVPVSRQFAAYCYNSSDTWTNSCIPEIITTKDPIFNTQTATQTTEFIVSDSTYSVASPYSTIPAPTTTPPAPASTSIPRRKKLICVTEVFMETSTMSTETEPFVENKAAFKNEAAGEGGVPTALLVLALLFFGAAAGLGFCYVKRYVKAFPFTNKNQQKEMIETKVVKEEKANDSNPNEESKKTDKNPEESKSPSKTTVRCLEAEV

REG1A refers to regenerating islet-derived protein 1 that belongs to afamily of REG (regenerating) glycoproteins, which are expressed inpancreatic acinar cells and act as both autocrine and paracrine growthfactors. The Reg gene family is a multigene family grouped into foursubclasses, types I, II, III and IV; REG1A gene is a type I subclassmember. Other family members: REG1B, REGL, PAP and this gene aretandemly clustered on chromosome 2p12 and may have arisen from the sameancestral gene by gene duplication. REG1A encodes a protein that issecreted by the exocrine pancreas. References to REG1 herein include twocommonly described genes, i.e. REG1A and REG1B, whose products are morethan 80% identical at the protein level, and are difficult todistinguish. Hence in the present invention, the method may quantify theexpression or concentration of just one of REG1A and REG1B. Inalternative embodiments of the invention, the expression orconcentration of both REG1A and REG1B may be separately quantified. In afurther embodiment of the invention, a quantification method may be usedin which REG1A and REG1B cannot be distinguished and hence they may bequantified together.

References to REG1A include NCBI (GenBank) reference sequence transcriptNM_002909.4 (GI:189491780), NCBI protein ID NP_002900.2 (GI:29725633),GeneID:5967 and HGNC:9951. This gene has 4 splice variants (retainedintrons), but only one is protein coding (166 amino acids).

(SEQ ID NO: 2) MAQTSSYFMLISCLMFLSQSQGQEAQTELPQARISCPEGTNAYRSYCYYFNEDRETWVDADLYCQNMNSGNLVSVLTQAEGAFVASLIKESGTDDFNVWIGLHDPKKNRRWHWSSGSLVSYKSWGIGAPSSVNPGYCVSLTSSTGFQKWK DVPCEDKFSFVCKFKN

References to REG1B include NCBI (GenBank) reference sequence transcriptNM_006507.3 (GI:189491779), NCBI protein ID NP_006498.1, GeneID:5968 andHGNC:9952 NCBI. There are 5 splice variants of this gene (retainedintrons), only two code for the proteins of 166 amino acids and 149amino acids.

(SEQ ID NO: 3) MAQTNSFFMLISSLMFLSLSQGQESQTELPNPRISCPEGTNAYRSYCYYFNEDPETWVDADLYCQNMNSGNLVSVLTQAEGAFVASLIKESSTDDSNVWIGLHDPKKNRRWHWSSGSLVSYKSWDTGSPSSANAGYCASLTSCSGFKKWK DESCEKKFSFVCKFKN

Note that references to “REG1” herein refer to both REG1A and REG1B,since generally either can be used. However, in some embodiments, bothREG1A and REG1B may be used.

TFF1 refers to trefoil factor 1. TFF1 belongs to a family ofgastrointestinal secretory peptides, which interact with mucins and areexpressed at increased levels during reconstitution and repair ofmucosal injury. They protect epithelial cells from apoptotic death andincrease their motility, but also play similar pivotal roles in cancercells, and are thus involved in the development and progression ofvarious cancer types. References to TFF1 include NCBI (GenBank)reference sequence transcript NM_003225.2 (GI:48928023), NCBI proteinNP_003216.1 (GI:4507451), Gene ID:7031 and HGNC:11755.

(SEQ ID NO: 4) MATMENKVICALVLVSMLALGTLAEAQTETCTVAPRERQNCGFPGVTPSQCANKGCCFDDTVRGVPWCFYPNTIDVPPEEECEF

The method of the invention can be performed in a qualitative format,which determines the presence or absence of a cancer marker protein inthe sample, but preferably in a quantitative format, which, in addition,provides a measurement of the quantity of cancer marker protein presentin the sample. The quantity of marker protein present in the sample maybe calculated using any of the above described techniques. In this case,prior to performing the assay, it may be necessary to draw a standardcurve by measuring the signal obtained using the same detection reactionthat will be used for the assay from a series of standard samplescontaining known concentrations of the cancer marker protein. Thequantity of cancer marker present in a sample to be screened can thenextrapolated from the standard curve.

Generally, an increase in one or more of the biomarkers in a test samplecompared to a control sample from a healthy patient indicates thepresence of chronic pancreatitis and/or PDAC. The thresholdconcentrations of each of the biomarkers may differ from patient topatient or population to population. Indicative protein concentrationsfrom unprocessed (“raw” or “crude”) urine samples are shown below:

Healthy CP PDAC LYVE1  ≤2 ng/ml 2-10 ng/ml  ≥10 ng/ml REG1A ≤120 ng/ml 120-500 ng/ml   ≥500 ng/ml REG1B ≤40 ng/ml 40-100 ng/ml  ≥100 ng/ml TFF1≤2.5 ng/ml  2.5-4 ng/ml   ≥4 ng/ml

For example, if a sample contains between 2 and 10 ng/ml LYVE1, between120 and 500 ng/ml REG1A, between 40 and 100 ng/ml REG1B and/or between2.5 to 5 ng/ml TFF1, chronic pancreatitis may be suspected. However,these ranges are indicative, and the skilled person will realise thatthe concentration of each protein will need to be considered in context,for example depending on the origin of the sample and any pre-processingof that sample that may have taken place. The more of the biomarkersthat fall within the relevant concentration thresholds, the more likelyit is the patient is healthy, has CP or has PDAC.

For example, if an unprocessed urine sample meets at least one of thefollowing criteria (optionally at least 2, 3 or 4 of the followingcriteria), chronic pancreatitis may be suspected:

-   -   a) between 2 and 10 ng/ml LYVE1    -   b) between 120 and 500 ng/ml REG1A    -   c) between 40 and 100 ng/ml REG1B and/or    -   d) between 2.5 to 5 ng/ml TFF1

If an unprocessed urine sample meets at least one of the followingcriteria (optionally at least 2, 3 or 4 of the following criteria), PDACmay be suspected:

-   -   a) more than 10 ng/ml LYVE1    -   b) more than 500 ng/ml REG1A    -   c) more than 100 ng/ml REG1B and/or    -   d) more than 5 ng/ml TFF1

Of course, methods of diagnosis using the biomarker panels of theinvention can be further confirmed by, for example, testing a biopsy forthe presence of PDAC, and/or the use of additional biomarkers (such asCA19.9).

In one embodiment of the invention, the method may comprise detectingthe level of expression or concentration of a protein selected from thegroup consisting of LYVE1, REG1 and TFF1, or combinations thereof, in aurine sample. In some embodiments of the invention, the method maycomprise quantifying the level of expression or concentration of onlyone of LYVE1, REG1 and TFF1 in a urine sample. In other embodiments ofthe invention, the method may comprise quantifying the level ofexpression or concentration of any two of LYVE1, REG1 and TFF1, forexample LYVE1 and REG1 (REG1A and/or REG1B), LYVE1 and TFF1, or REG1(REG1A and/or REG1B) and TFF1. In some embodiments of the invention, themethod may comprise detecting the level of expression of all of LYVE1,REG1 (REG1A and/or REG1B) and TFF1. CA19.9 may also be quantified insome embodiments of the invention.

Types of Pancreatic Cancer

The present invention is useful in the diagnosis of PDAC. The PDAC maybe early stage PDAC, for example stage I or stage II PDAC, or it may belate-stage PDAC, for example stage III or stage IV PDAC. However, thepresent invention is particularly useful in detecting early-stage PDAC,in particular stage I to stage IIA PDAC.

In one embodiment of the invention there is thus provided a method ofdiagnosing or detecting stage I or stage II PDAC, comprising detectingthe level of expression or concentration of LYVE1, REG1 (REG1A and/orREG1B) and/or TFF1 in a urine sample and comparing each of the detectedexpression levels with a reference or references. The methods inparticular may determine the presence of stage I and/or stage II PDACand distinguish these from healthy samples, patients having CP andpatients having IPMNs.

Classification of PDAC can be done according to the The American JointCommittee on Cancer (AJCC) tumour-nodes-metastasis (TNM) staging system.The T score describes the size of the main (primary) tumour and whetherit has grown outside the pancreas and into nearby organs. The N scoredescribes the spread to nearby (regional) lymph nodes. The M scoreindicates whether the cancer has metastasized (spread) to other organsof the body:

Tx, T0, Tis: See TNM System

T1: tumour <2 cm in greatest dimension, limited to pancreas

T2: tumour >2 cm in greatest dimension, limited to pancreas

T3: extension beyond pancreas, no involvement of SMA or coeliac axis

T4: involvement of SMA or coeliac axis

Regional Lymph Nodes (N)

Nx: nodes cannot be assessed

N0: no evidence of nodal involvement

N1: regional nodal metastases present

Metastases (M)

Mx: presence of metastases cannot be assessed

M0: no evidence of metastases

M1: distant metastases present

Stage I PDAC is the earliest stage, where cancer is confined to thepancreas, and there is no cancer in the lymph nodes. In Stage II, thecancer is locally invasive. Cancer in both of these stages is stillresectable; currently, fewer than 1 in 5 cancers of the pancreas (<20%)are diagnosed at stage I/II. References to stage II PDAC herein includestage IIA and IIB. In stage III, cancer has spread beyond pancreas andis in large blood vessels, so unresectable. Stage IV cancer hasmetastasized to distant sites (and again not treatable by surgery).References herein to detecting or diagnosing PDAC generally refer todetecting or diagnosing each stage PDAC, in particular stage I or stageII PDAC. Such methods are particularly useful given the cancer is stilltreatable by resection at this stage and survival rates are muchimproved.

With reference to the TNM score, the stage groupings are:

-   -   stage 0: Tis N0 M0    -   stage Ia: T1 N0 M0    -   stage Ib: T2 N0 M0    -   stage IIa: T3 N0 M0    -   stage IIb: T1, T2 or T3 with N1 M0    -   stage III: T4 and M0 (any N)    -   stage IV: M1 (any T any N)        Biological Samples

The biological sample may be a urine sample, a whole blood sample, aserum sample or a biopsy (such as a pancreatic tissue sample), althoughurine samples are particularly useful. The method may include a step ofobtaining or providing the biological sample, or alternatively thesample may have already been obtained from a patient, for example in exvivo methods.

Biological samples obtained from a patient can be stored until needed.Suitable storage methods include freezing within two hours ofcollection. Maintenance at −80° C. can be used for long-term storage.

The sample may be processed prior to determining the level of expressionof the gene(s)/protein(s). The sample may be subject to enrichment (forexample to increase the concentration of the biomarkers beingquantified), centrifugation or dilution. In other embodiments, thesamples do not undergo any pre-processing and are used unprocessed.

In some embodiments of the invention, the biological sample may beenriched for the protein biomarkers prior to detection andquantification (i.e. measurement). The step of enrichment can be anysuitable pre-processing method step to increase the concentration ofprotein in the sample. For example, the step of enrichment may comprisecentrifugation and/or filtration to remove cells or unwanted analytesfrom the sample.

The methods of the invention may be carried out on one test sample froma patient. Alternatively, a plurality of test samples may be taken froma patient, for example 2, 3, 4 or 5 samples. Each sample may besubjected to a single assay to quantify one of the biomarker panelmembers, or alternatively a sample may be tested for all of thebiomarkers being quantified.

Methods and Uses of the Invention

The present invention provides a panel of biomarkers useful in thedetection of PDAC and, in particular, differentially diagnosing earlystage PDAC from late stage PDAC, CP and pancreatitis.

In one embodiment of the invention, the method comprises the steps of:

-   -   a) detecting biomarkers of interest, in particular proteins, in        a biological sample obtained from a patient; and    -   b) quantifying the expression level or concentration of each of        the biomarkers

The biomarkers belong to the biomarker panel of the invention. Hence,detection/quantification comprises detection/quantification of one ormore of the following biomarkers:

-   -   1) LYVE1    -   2) REG1 (REG1A and/or REG1B)    -   3) TFF1    -   4) CA19.9

In one preferred embodiment, the invention comprises analysis of atleast one biomarker selected from the group consisting of LYVE1, REG1and TFF1, in combination with the biomarker CA19.9.

The level of expression of a gene or protein can be determined in anumber of ways. Levels of expression may be determined by, for example,quantifying the biomarkers by determining the concentration of proteinin the sample (such as a urine sample). Alternatively, the amount ofmRNA in the sample (such as a tissue sample) may be determined. Once thelevel of expression or concentration has been determined, the level canbe compared to a previously measured level of expression orconcentration (either in a sample from the same subject but obtained ata different point in time, or in a sample from a different subject, forexample a healthy subject, i.e. a control or reference sample) todetermine whether the level of expression or protein concentration ishigher or lower in the sample being analysed.

In the present invention, an increase in expression (and hence proteinconcentration) compared to a healthy patient in one or all of LYVE1,REG1 and/or TFF1 is indicative of PDAC or CP. The panel is particularlyuseful at correctly diagnosing early-stage PDAC. False negatives andfalse positives can also be minimised by utilising all panel memberstogether. CA19.9 may also be included in the biomarker panel to furtherreduce the incidence of false positives or false negatives.

Methods for detecting the levels of protein expression include anymethods known in the art. For example, protein levels can be measuredindirectly using DNA or mRNA arrays. Alternatively, protein levels canbe measured directly by measuring the level of protein synthesis ormeasuring protein concentration.

DNA and mRNA arrays (microarrays) comprise a series of microscopic spotsof DNA or RNA oligonucleotides, each with a unique sequence ofnucleotides that are able to bind complementary nucleic acid molecules.In this way the oligonucleotides are used as probes to which only thecorrect target sequence will hybridise under high-stringency condition.In the present invention, the target sequence is either the coding DNAsequence or unique section thereof, corresponding to the protein whoseexpression is being detected, or the target sequence is the transcribedmRNA sequence, or unique section thereof, corresponding to the proteinwhose expression is being detected.

Directly measuring protein expression and identifying the proteins beingexpressed in a given sample can be done by any one of a number ofmethods known in the art. For example, 2-dimensional polyacrylamide gelelectrophoresis (2D-PAGE) has traditionally been the tool of choice toresolve complex protein mixtures and to detect differences in proteinexpression patterns between normal and diseased tissue. Differentiallyexpressed proteins observed between normal and tumour samples areseparate by 2D-PAGE and detected by protein staining and differentialpattern analysis. Alternatively, 2-dimensional difference gelelectrophoresis (2D-DIGE) can be used, in which different proteinsamples are labelled with fluorescent dyes prior to 2D electrophoresis.After the electrophoresis has taken place, the gel is scanned with theexcitation wavelength of each dye one after the other. This technique isparticularly useful in detecting changes in protein abundance, forexample when comparing a sample from a healthy subject and a sample forma diseased subject.

Commonly, proteins subjected to electrophoresis are also furthercharacterised by mass spectrometry methods. Such mass spectrometrymethods can include matrix-assisted laser desorption/ionisationtime-of-flight (MALDI-TOF).

MALDI-TOF is an ionisation technique that allows the analysis ofbiomolecules (such as proteins, peptides and sugars), which tend to befragile and fragment when ionised by more conventional ionisationmethods. Ionisation is triggered by a laser beam (for example, anitrogen laser) and a matrix is used to protect the biomolecule frombeing destroyed by direct laser beam exposure and to facilitatevaporisation and ionisation. The sample is mixed with the matrixmolecule in solution and small amounts of the mixture are deposited on asurface and allowed to dry. The sample and matrix co-crystallise as thesolvent evaporates.

Protein microarrays can also be used to directly detect proteinexpression. These are similar to DNA and mRNA microarrays in that theycomprise capture molecules fixed to a solid surface. Capture moleculesare most commonly antibodies specific to the proteins being detected,although antigens can be used where antibodies are being detected inserum. Further capture molecules include proteins, aptamers, nucleicacids, receptors and enzymes, which might be preferable if commercialantibodies are not available for the protein being detected. Capturemolecules for use on the protein arrays can be externally synthesised,purified and attached to the array. Alternatively, they can besynthesised in-situ and be directly attached to the array. The capturemolecules can be synthesised through biosynthesis, cell-free DNAexpression or chemical synthesis. In-situ synthesis is possible with thelatter two. There is therefore provided a protein microarray comprisingcapture molecules (such as antibodies) specific for each of thebiomarkers being quantified immobilised on a solid support. In oneembodiment of the invention, the microarray comprises capture moleculesspecific for each of LYVE1, REG1 (REG1A and/or REG1B) and TFF1 proteins.

Once captured on a microarray, detection methods can be any of thoseknown in the art. For example, fluorescence detection can be employed.It is safe, sensitive and can have a high resolution. Other detectionmethods include other optical methods (for example colorimetricanalysis, chemiluminescence, label free Surface Plasmon Resonanceanalysis, microscopy, reflectance etc.), mass spectrometry,electrochemical methods (for example voltametry and amperometry methods)and radio frequency methods (for example multipolar resonancespectroscopy).

Additional methods of determining protein concentration include massspectrometry and/or liquid chromatography, such as LC-MS, UPLC, or atandem UPLC-MS/MS system.

Once the level of expression or concentration has been determined, thelevel can be compared to a previously measured level of expression orconcentration (either in a sample from the same subject but obtained ata different point in time, or in a sample from a different subject, forexample a healthy subject, i.e. a control or reference sample) todetermine whether the level of expression or concentration is higher orlower in the sample being analysed. The methods of the invention mayfurther comprise a step of correlating said detection or quantificationwith a control or reference to determine if PDAC is present or not. Saidcorrelation step may also detect the presence of particular types ofPDAC and to distinguish these patients from healthy patients, in whichno PDAC or pancreatic cancer is present, or from patients suffering fromCP or intraductal papillary mucinous neoplasms (IPMNs). For example, themethods may detect early stage PDAC, in particular stage I and/or stageII PDAC. Said step of correlation may include comparing the amount ofone, two, three, four or more of the panel biomarkers with the amount ofthe corresponding biomarker(s) in a reference sample, for example in abiological sample taken from a healthy patient. Generally the methoddoes not include the steps of determining the amount of thecorresponding biomarker in a reference sample, and instead such valueswill have been previously determined. However, in some embodiments themethods of the invention may include carrying out the method steps froma healthy patient who is used as a control. Alternatively, the methodmay use reference data obtained from samples from the same patient at aprevious point in time. In this way, the effectiveness of any treatmentcan be assessed and a prognosis for the patient determined.

Internal controls can be also used, for example quantification of one ormore different biomarkers not part of the biomarker panel. This mayprovide useful information regarding the relative amounts of thebiomarkers in the sample, allowing the results to be adjusted for anyvariances according to different populations or changes introducedaccording to the method of sample collection, processing or storage.

As would be apparent to a person of skill in the art, any measurementsof analyte concentration or expression may need to be normalised to takein account the type of test sample being used and/or and processing ofthe test sample that has occurred prior to analysis. Data normalisationalso assists in identifying biologically relevant results. Invariantbiomarkers may be used to determine appropriate processing of thesample. Differential expression calculations may also be conductedbetween different samples to determine statistical significance.

In general, the methods of the present invention may comprise the stepsof:

-   -   a) providing a biological sample, such as a urine sample;    -   b) optionally processing the sample, for example to enrich the        sample for miRNAs; and    -   c) quantification of the biomarkers.

The methods may further comprise the step of:

-   -   d) comparison of the level of protein expression from step d)        with a control or reference sample.

In some embodiments of the invention, the step of quantification maycomprise the following steps:

-   -   a) contacting the sample with a binding partner that        specifically binds to the biomarker of interest    -   b) quantifying the amount of biomarker-binding partner to        determine the amount of the biomarker present in the original        sample.

The present invention therefore provides a reaction mixture, comprisingeither the protein biomarkers of interest, or a biological sample (suchas a urine sample) containing the protein biomarkers of interest,wherein the protein biomarkers of interest are bound to respectivebinding partners specific to the protein biomarkers. The bindingpartners may be, for example, antibodies that specifically bind to theprotein biomarkers of interest. In one embodiment, the reaction mixturecomprises LYVE1, REG1 and TFF1 proteins bound to respective selectivebinding molecules, such as antibodies. The selective binding moleculesare exogenous.

In some embodiments of the invention, the method comprises correlatingthe measured biomarkers with PDAC, in particular stage I or stage IIPDAC. The present invention therefore provides a method of qualifyingpancreatic disease in a patient, or determining the presence or absenceof pancreatic disease, the method comprising measuring the abundance ofone or more relevant biomarkers in a biological sample (such as a urinesample) and correlating the measured biomarkers with a stage of disease.The stage of disease may be chronic pancreatitis, stage I PDAC, or alater stage of PDAC. Alternatively, it may be determined the patient ishealthy, i.e. pancreatic disease is absent.

As noted above, the method of the invention can be carried out using anexogenous binding molecules or reagents specific for the protein orproteins being detected. “Exogenous” refers to the fact the bindingmolecules or reagents have been added to the sample undergoing analysis.Binding molecules and reagents are those molecules that have an affinityfor the protein or proteins being detected such that they can formbinding molecule/reagent-protein complexes that can be detected usingany method known in the art. The binding molecule of the invention canbe an antibody, an antibody fragment, a protein or an aptamer ormolecularly imprinted polymeric structure. Methods of the invention maycomprise contacting the biological sample with an appropriate bindingmolecule or molecules. Said binding molecules may form part of a kit ofthe invention, in particular they may form part of the biosensors of inthe present invention.

Antibodies can include both monoclonal and polyclonal antibodies and canbe produced by any means known in the art. Techniques for producingmonoclonal and polyclonal antibodies which bind to a particular proteinare now well developed in the art. They are discussed in standardimmunology textbooks, for example in Roitt et al., Immunology, secondedition (1989), Churchill Livingstone, London. Polyclonal antibodies canbe raised by stimulating their production in a suitable animal host(e.g. a mouse, rat, guinea pig, rabbit, sheep, chicken, goat or monkey)when the antigen is injected into the animal. If necessary, an adjuvantmay be administered together with the antigen. The antibodies can thenbe purified by virtue of their binding to antigen or as describedfurther below. Monoclonal antibodies can be produced from hybridomas.These can be formed by fusing myeloma cells and B-lymphocyte cells whichproduce the desired antibody in order to form an immortal cell line.This is the well-known Kohler & Milstein technique (Kohler & Milstein(1975) Nature, 256:52-55). The antibodies may be human or humanised, ormay be from other species.

After the preparation of a suitable antibody, it may be isolated orpurified by one of several techniques commonly available (for example,as described in Harlow & Lane eds., Antibodies: A Laboratory Manual(1988) Cold Spring Harbor Laboratory Press). Generally, suitabletechniques include peptide or protein affinity columns, high performanceliquid chromatography (HPLC) or reverse phase HPLC (RP-HPLC),purification on Protein A or Protein G columns, or combinations of thesetechniques. Recombinant and chimeric antibodies can be preparedaccording to standard methods, and assayed for specificity usingprocedures generally available, including ELISA, ABC, dot-blot assays.

The present invention includes antibody derivatives which are capable ofbinding to antigen. Thus the present invention includes antibodyfragments and synthetic constructs. Examples of antibody fragments andsynthetic constructs are given in Dougall et al. (1994) TrendsBiotechnol, 12:372-379.

Antibody fragments or derivatives, such as Fab, F(ab′)₂ or Fv may beused, as may single-chain antibodies (scAb) such as described by Hustonet al. (993) Int Rev Immunol, 10:195-217, domain antibodies (dAbs), forexample a single domain antibody, or antibody-like single domainantigen-binding receptors. In addition antibody fragments andimmunoglobulin-like molecules, peptidomimetics or non-peptide mimeticscan be designed to mimic the binding activity of antibodies. Fvfragments can be modified to produce a synthetic construct known as asingle chain Fv (scFv) molecule. This includes a peptide linkercovalently joining VH and VL regions which contribute to the stabilityof the molecule. The present invention therefore also extends to singlechain antibodies or scAbs.

Other synthetic constructs include CDR peptides. These are syntheticpeptides comprising antigen binding determinants. These molecules areusually conformationally restricted organic rings which mimic thestructure of a CDR loop and which include antigen-interactive sidechains. Synthetic constructs also include chimeric molecules. Thus, forexample, humanised (or primatised) antibodies or derivatives thereof arewithin the scope of the present invention. An example of a humanisedantibody is an antibody having human framework regions, but rodenthypervariable regions. Synthetic constructs also include moleculescomprising a covalently linked moiety which provides the molecule withsome desirable property in addition to antigen binding. For example themoiety may be a label (e.g. a detectable label, such as a fluorescent orradioactive label) or a pharmaceutically active agent.

In those embodiments of the invention in which the binding molecule isan antibody or antibody fragment, the method of the invention can beperformed using any immunological technique known in the art. Forexample, ELISA, radio immunoassays or similar techniques may beutilised. In general, an appropriate autoantibody is immobilised on asolid surface and the sample to be tested is brought into contact withthe autoantibody. If the cancer marker protein recognised by theautoantibody is present in the sample, an antibody-marker complex isformed. The complex can then be directed or quantitatively measuredusing, for example, a labelled secondary antibody which specificallyrecognises an epitope of the marker protein. The secondary antibody maybe labelled with biochemical markers such as, for example, horseradishperoxidase (HRP) or alkaline phosphatase (AP), and detection of thecomplex can be achieved by the addition of a substrate for the enzymewhich generates a colorimetric, chemiluminescent or fluorescent product.Alternatively, the presence of the complex may be determined by additionof a marker protein labelled with a detectable label, for example anappropriate enzyme. In this case, the amount of enzymatic activitymeasured is inversely proportional to the quantity of complex formed anda negative control is needed as a reference to determining the presenceof antigen in the sample. Another method for detecting the complex mayutilise antibodies or antigens that have been labelled withradioisotopes followed by a measure of radioactivity. Examples ofradioactive labels for antigens include ³H, ¹⁴C and ¹²⁵I.

Aptamers are oligonucleotides or peptide molecules that bind a specifictarget molecule. Oligonucleotide aptamers include DNA aptamer and RNAaptamers. Aptamers can be created by an in vitro selection process frompools of random sequence oligonucleotides or peptides. Aptamers can beoptionally combined with ribozymes to self-cleave in the presence oftheir target molecule.

Aptamers can be made by any process known in the art. For example, aprocess through which aptamers may be identified is systematic evolutionof ligands by exponential enrichment (SELEX). This involves repetitivelyreducing the complexity of a library of molecules by partitioning on thebasis of selective binding to the target molecule, followed byre-amplification. A library of potential aptamers is incubated with thetarget protein before the unbound members are partitioned from the boundmembers. The bound members are recovered and amplified (for example, bypolymerase chain reaction) in order to produce a library of reducedcomplexity (an enriched pool). The enriched pool is used to initiate asecond cycle of SELEX. The binding of subsequent enriched pools to thetarget protein is monitored cycle by cycle. An enriched pool is clonedonce it is judged that the proportion of binding molecules has risen toan adequate level. The binding molecules are then analysed individually.SELEX is reviewed in Fitzwater & Polisky (1996) Methods Enzymol,267:275-301.

Thus, in one embodiment of the invention, there is provided a method ofdiagnosing PDAC or CP comprising contacting a biological sample (such asa urine sample) from a patient with reagents or binding moleculesspecific for the biomarker proteins being quantified, and measuring theabundance of protein-reagent or protein-binding molecule complexes, andcorrelating the abundance of protein-reagent or protein-binding moleculecomplexes with the concentration of the relevant protein in thebiological sample. For example, in one embodiments of the invention, themethod comprises the steps of:

-   -   a) contacting a biological sample (such as a urine sample) with        reagents or binding molecules specific for one or more of LYVE1,        REG1 (REG1A and/or REG1B) and TFF1;    -   b) quantifying the abundance of protein-reagent or        protein-binding molecule complexes for one or more of LYVE1,        REG1 (REG1A and/or REG1B) and TFF1; and    -   c) correlating the abundance of protein-reagent or        protein-binding molecule complexes with the concentration of one        or more of the proteins LYVE1, REG1 (REG1A and/or REG1B) and        TFF1 in the biological sample.

The method may further comprise the step of d) comparing theconcentration of the proteins in step c) with a reference to determinethe presence or absence of PDAC or CP. The patient can then be treatedaccordingly. In some embodiments of the invention, the methods comprisecontacting the biological sample with reagents or binding moleculesspecific for one, two or three of LYVE1, REG1 (REG1A and/or REG1B) andTFF1, and, in some embodiments, all of the panel biomarkers. In furtherembodiments, CA19.9 may be included by additionally contacting thebiological sample with a reagent or binding molecule specific forCA19.9. Suitable reagents or binding molecules may include an antibodyor antibody fragment, an enzyme, a nucleic acid, an organelle, a cell, abiological tissue, imprinted molecule or a small molecule. Such methodsmay be carried out using kits or biosensors of the invention.

The present invention also provides a method of diagnosis for pancreaticductal adenocarcinoma comprising detecting the level of expression orconcentration of a protein selected from the group consisting of LYVE1,REG1 (REG1A and/or REG1B) and TFF1, or combinations thereof, in abiological sample, in particular a urine sample. In one embodiment ofthe invention, the method may comprise detecting the level of expressionof two proteins selected from the group consisting of LYVE1, REG1 (REG1Aand/or REG1B) and TFF1, in a biological sample, in particular a urinesample. In some embodiments of the invention, the method may comprisedetecting the level of expression of LYVE1, TFF1 and either or both ofREG1A and REG1B in a biological sample, in particular a urine. Since thebiomarker panel of the invention can also detect chronic pancreatitis,analogous methods of diagnosis of CP are also provided.

The presence of pancreatic ductal adenocarcinoma can be determined bydetecting an increase in gene expression or protein concentration ascompared with the level of expression or protein concentration of thecorresponding genes or proteins in samples taken from healthy controlsubjects. In addition, the level of expression or concentration can beused to distinguish between PDAC and CP. This can be achieved bycomparing the level of expression or concentration found in the testsample with that seen in patients presenting with CP (or to areference). Furthermore, the biomarkers can be used to distinguishbetween PDAC and intraductal papillary mucinous tumours (IPMNs), Thiscan be done by comparing the level of expression or concentration foundin the test sample with that seen in patients presenting with IPMNs (orto a reference).

In a further embodiment of the invention there is provided a proteinselected from the group consisting of LYVE1, REG1 (REG1A and/or REG1B)and TFF1, or a combination thereof, for use in diagnosing pancreaticductal adenocarcinoma (PDAC) or CP. In one embodiment of the invention,there is provided the combination of two of LYVE1, REG1 and TFF1 for usein the diagnosis of PDAC or CP (for example LYVE1 and REG1 (REG1A and/orREG1B), LYVE1 and TFF1, or REG1 (REG1A and/or REG1B) and TFF1). Inanother embodiment of the invention, there is the provided thecombination of three of LYVE1, REG1 (REG1A and/or REG1B) and TFF1 foruse in the diagnosis of PDAC or CP. There is also provided thecombination of all four of LYVE1, REG1A, REG1B and TFF1 for use in thediagnosis of PDAC or CP. These biomarker panels may additionally becombined with CA19.9 in some embodiments of the present invention.

In another embodiment of the invention there is provided a method oftreating or preventing PDAC in a patient, comprising quantifying one ormore biomarkers selected from the group consisting of LYVE1, REG1 (REG1Aand/or REG1B) and TFF1 in a biological sample (in particular a urinesample) obtained from a patient, comparing the values to a reference foreach of the quantified biomarkers, and, if the detected values aregreater than the reference (or if PDAC is diagnosed or suspected),administering treatment for PDAC. Methods of treating PDAC may includeresecting the pancreatic tumour and/or administering chemotherapy and/orradiotherapy to the patient. The biomarkers may be quantified bydetermining the level of gene expression (for example determining themRNA concentration) or by determining the protein concentration. In someembodiments, each of LYVE1, REG1 (REG1A and/or REG1B) and TFF1 arequantified. CA19.9 may also be quantified in such methods.

The methods of treating PDAC of the present invention are particularlyuseful in the treatment of early-stage PDAC. The methods of preventingPDAC are particularly useful in the prevention of late-stage PDAC. Insome embodiments, the methods of treatment are performed on patients whohave been identified as having a particular concentration of thebiomarker proteins in a biological sample. Said concentration is onethat it is indicative of PDAC. Accordingly, a method of treating PDAC orCP is provided, comprising resecting any pancreatic tumour and/oradministering chemotherapy and/or radiotherapy in a patient in whom PDACor CP has been diagnosed using a method of the present invention.

In a still further embodiment of the invention there is provided amethod for determining the suitability of a patient for treatment forPDAC or CP, comprising detecting the level of expression of a proteinselected from the group consisting of LYVE1, REG1 (REG1A and/or REG1B)and TFF1, or combinations thereof, in a urine sample, comparing thelevel of expression with a control, and deciding whether or not toproceed with treatment for PDAC or CP if PDAC or CP is diagnosed orsuspected.

In some embodiments of the invention, the methods may further comprisetreating a patient for PDAC or CP if PDAC or CP is detected orsuspected. If PDAC or CP is detected or suspect based on the analysis ofa urine, blood or serum sample, the presence of PDAC or CP can beconfirmed by, for example, detecting the presence and/or amount of thebiomarkers in a sample of pancreatic tissue. Methods of the inventionmay therefore further comprise a step of detecting or determining theamount of a biomarker in a pancreatic tissue sample. The pancreatictissue sample may have been obtained previously from a patient, or themethod may comprise a step of obtaining or providing said pancreatictissue sample. Analysis of pancreatic tissue samples may also comprise ahistological analysis.

If possible, treatment for PDAC (in particular stage I and stage IIPDAC) involves resecting the tumour. Treatment may alternatively oradditional involve treatment by chemotherapy and/or radiotherapy.Treatment by chemotherapy may include administration of gemcitabineand/or Folfirinox. Folfirinox is a combination of fluorouracil (5-FU),irinotecan, oxaliplatin and folinic acid (leucovorin). Treatmentregimens involving Folfirinox may comprise administration ofoxaliplatin, followed by folinic acid, followed by irinotecan(alternatively irinotecan may be administered at the same time asfolinic acid), followed by 5-FU.

There is also provided a method of monitoring a patient's response totherapy, comprising determining the level of expression of at least oneof the biomarkers of interest in a biological sample obtained from apatient that has previously received therapy PDAC (for examplechemotherapy and/or radiotherapy). In some embodiments, the level ofexpression is compared with the level of expression for the samebiomarker or biomarkers in a sample obtained from a patient beforereceiving the therapy. A decision can then be made on whether tocontinue the therapy or to try an alternative therapy based on thecomparison of the levels of expression.

In one embodiment, there is therefore provided a method comprising:

-   -   a) determining the level of expression of at least one biomarker        of interest, or combination thereof, in a biological sample        obtained from a patient that has previously received therapy for        pancreatic cancer or PDAC;    -   b) comparing the level of expression of the biomarker or        biomarkers determined in step a) with a previously determined        level of expression of the same biomarker or biomarkers (i.e.        determined prior to the treatment for pancreatic cancer or        PDAC); and    -   c) maintaining, changing or withdrawing the therapy for        pancreatic cancer or PDAC.

The method may comprise a prior step of administering the therapy forpancreatic cancer or PDAC to the patient. In another embodiment, themethod may also comprise a pre-step of determining the level ofexpression of at least one biomarker of interest, or combinationthereof, in a biological sample obtained from the same patient prior toadministration of the therapy. In step c), the therapy for pancreaticcancer or PDAC may be maintained if an appropriate adjustment in thelevel(s) of expression of the biomarker or biomarkers is determined. Forexample, if there is a reduction in the expression of one or more of thebiomarkers found to be up-regulated in pancreatic cancer or PDAC, thentreatment may be maintained. If the levels of expression have alteredsufficiently, for example back to what may be considered healthy orlow-risk levels, then treatment for pancreatic cancer or PDAC may bewithdrawn. If the levels of expression are unchanged or have worsened(for example there is an increase in the expression of one or more ofthe biomarkers found to be up-regulated in pancreatic cancer or PDAC),this may be indicative of a worsening of the patient's condition, andhence an alternative therapy for pancreatic cancer or PDAC may beattempted. In this way, drug candidates useful in the treatment ofpancreatic cancer or PDAC (in particular early stage PDAC) can bescreened.

In another embodiment of the invention, there is provided a methodidentifying a drug useful for the treatment of PDAC, comprising:

-   -   (a) quantifying the expression or concentration of one or more        proteins selected from the group consisting of LYVE1, REG1 and        TFF1 in a biological sample obtained from a patient;    -   (b) administering a candidate drug to the patient;    -   (c) quantifying the expression or concentration of one or more        proteins selected from the group consisting of LYVE1, REG1 and        TFF1 in a biological sample obtained from the same patient at a        point in time after administration of the candidate drug; and    -   (d) comparing the value determined in step (a) with the value        determined in step (c), wherein a decrease in the level of        expression or concentration of one or more of the proteins        between the two samples identifies the drug candidate as a        possible treatment for PDAC. In some embodiments, the method        uses all of the panel biomarkers proteins, i.e. all of LYVE1,        REG1 and TFF1 must be quantified in step (a) and step (c). In        some embodiments, the biological sample is a urine sample. In        some embodiments, the drug is a compound, an antibody or        antibody fragment.        Kits and Biosensors

In a still further embodiment of the invention there is provided a kitof parts for testing for pancreatic ductal adenocarcinoma comprising ameans for quantifying the expression or concentration of LYVE1, REG1(REG1A and/or REG1B) or TFF1, or combinations thereof. The means may beany suitable detection means.

For example, the means may be a biosensor. In some embodiments, themeans may comprise a dipstick coated with a membrane that is bound to anunlabelled binding molecule (such as an antibody or antibody fragment)with specific affinity for the protein being detected on a firstsection. The membrane may also have a section that is blocked with anon-reactive protein to prevent any molecules binding to that part ofthe membrane. The membrane may also have a section to which is bound theprotein that is being detected in the sample. The dipstick may beequipped to detect more than one protein in a single assay by havingdifferent sections dedicated to the detection of different proteins ofthe invention, such that each further protein to be detected has acorresponding antibody capable of specifically binding that furtherprotein bound on one section of the dipstick, and optionally the furtherprotein to be detected bound on another section of the dipstick. The kitmay also comprise a container for the sample or samples and/or a solventfor extracting the biomarkers from the biological sample. The kit mayalso comprise instructions for use.

In some embodiments of the invention, there is provided a kit of partsfor diagnosing pancreatic ductal adenocarcinoma or CP comprising a meansfor detecting the expression or concentration of at least two of LYVE1,REG1 and TFF1. In further embodiments of the invention, there isprovided a kit of parts for diagnosing pancreatic ductal adenocarcinomaor CP comprising a means for detecting the expression or concentrationof all three of LYVE1, REG1 and TFF1. The means for detecting thebiomarkers may be reagents that specifically bind to or react with thebiomarkers being quantified.

The kit of parts of the invention may be a biosensor. A biosensorincorporates a biological sensing element and provides information on abiological sample, for example the presence (or absence) orconcentration of an analyte. Specifically, they combine a biorecognitioncomponent (a bioreceptor) with a physiochemical detector for detectionand/or quantification of an analyte (such as a protein).

The bioreceptor specifically interacts with or binds to the analyte ofinterest and may be, for example, an antibody or antibody fragment, anenzyme, a nucleic acid, an organelle, a cell, a biological tissue,imprinted molecule or a small molecule. The bioreceptor may beimmobilised on a support, for example a metal, glass or polymer support,or a 3-dimensional lattice support, such as a hydrogel support.

Biosensors are often classified according to the type of biotransducerpresent. For example, the biosensor may be an electrochemical (such as apotentiometric), electronic, piezoelectric, gravimetric, pyroelectricbiosensor or ion channel switch biosensor. The transducer translates theinteraction between the analyte of interest and the bioreceptor into aquantifiable signal such that the amount of analyte present can bedetermined accurately. Optical biosensors may rely on the surfaceplasmon resonance resulting from the interaction between the bioreceptorand the analyte of interest. The SPR can hence be used to quantify theamount of analyte in a test sample. Other types of biosensor includeevanescent wave biosensors, nanobiosensors and biological biosensors(for example enzymatic, nucleic acid (such as DNA), antibody,epigenetic, organelle, cell, tissue or microbial biosensors).

Dipsticks are another example of biosensor. The dipsticks of theinvention may comprise a membrane. The dipsticks may further comprise afirst section to which is bound an unlabelled antibody with specificaffinity for the protein whose expression is being detected, a secondsection that is blocked with a non-reactive protein and a third sectionto which is bound the protein whose expression is being detected.

Dipstick techniques known in the art can be used to quickly andeffectively carry out the method of the invention. Dipstick techniquesinclude the following. A labelled antibody, for example labelled withformazan, having a specific affinity for the protein (antigen) beingdetected is dissolved in a sample of test fluid. A dipstick on which anitrocellulose membrane is mounted is immersed in the reaction mixture.The membrane has one section on which non-labelled antibodies having aspecific affinity for that antigen are bound. The second section is freeof antibodies and is blocked with a non-reactive protein to preventbinding of labelled antibodies to the membrane. A third section of thedipstick is provided on which the antigen is bound. Reactions take placebetween the free antigen in the test fluid and the non-labelled antibodybonded to the membrane, as well as between the free antigen and thelabelled antibody that was added to the sample. This results in asandwich of non-labelled bonded antibody/antigen/labelled antibody overthe first section of the membrane. A reaction also takes place betweenthe labelled antibody and the bound antigen over the third section. Noreaction takes places over the second section of the membrane.

The reaction is allowed to proceed for a fixed period of time or untilcompletion is determined visually. Since formazan is a highly coloureddye, the reacted formazan-labelled antibody imparts colour to the thirdsection, and if the antigen is present in the test fluid, to the firstsection as well. Since no reaction takes place over the second section,no colour is developed over that section. The second section thus actsas a negative control. In cases in which colour is imparted across theentire membrane, including the second section due to absorption ofun-reacted formazan particles and, to a minor extent, of un-reactedformazan-labelled antibody, presence of the antigen is indicated by adifference in colour between the first and second sections of themembrane. The third section is provided as a positive control bydemonstrating that the appropriate reactions are in fact taking place.

The length of time that the dipstick is immersed in the mixture is thatwhich allows a difference in colour intensity to develop between thefirst and second sections of the membrane if the antigen is present. Formost antibody-antigen reactions, colour development is essentiallycomplete within 30 to 60 minutes. If desired, colour development of thedipstick can be monitored by simply removing the dipstick, visuallychecking the colour intensity across the first section of the membrane,and then re-immersing the dipstick if required. When no further changein colour intensity is seen, the reaction can be deemed complete.

The dipstick can be prepared by any conventional methods known in theart. For example, a nitrocellulose membrane is mounted at the lower endof the dipstick. A solution containing non-labelled primary antibody isapplied over one section of the membrane to bind primary antibodies tothe membrane. A solution containing a blocking agent (for example 1%serum albumin) is applied over another section of the membrane toprevent subsequent bonding of the primary protein to the membrane.

Dipsticks can be equipped for the detection of more than one protein ata time by including further sections to which are bound un-labelledantibodies with specific affinity for the further protein or proteinsbeing detected and, optionally, a section to which is bound the proteinbeing detected. In such cases, labelled antibodies with specificaffinity for the protein being detected can be added to the sample suchthat their binding to the further section of the dipstick, and hencetheir presence in the sample, be detected. The antibodies can belabelled with the same dye or with a different dye. Suitable dyes, otherthan formazan, include acid dyes (for example anthraquinone ortriphenylmethane), azo dyes (for example methyl orange or disperseorange 1), fluorescent dyes (for example fluorescein or rhodamine) orany other suitable dye known in the art such as coomassie blue, amidoblack, toluidine blue, fast green, Indian ink, silver nitrate and silverlactate. It is also apparent that the pre-labelled primary proteinreactant is not limited to antibodies, but can include any protein orother molecule having specific affinity for a second protein to bedetected in a sample.

The invention also provides protein microarrays (also known as proteinchips) comprising capture molecules (such as antibodies) specific foreach of the biomarkers being quantified, wherein the capture moleculesare immobilised on a solid support. The solid support may be a slide, amembrane, a bead or microtitre plate. The slide may be a glass slide.The membrane may be a nitrocellulose membrane. The array may be aquantitative multiplex ELISA array. The microarrays are useful in themethods of the invention.

In particular, the present invention provides a combination of bindingmolecules, wherein the each binding molecule specifically binds adifferent target analyte, and the combination of analytes the bindingmolecules specifically bind to LYVE1, REG1 or TFF1, or combinationsthereof, and optionally CA19.9.

The binding molecules may be present on a solid substrate, such anarray. The binding molecules may all be present on the same solidsubstrate. Alternatively, the binding molecules may be present ondifferent substrates. In some embodiments of the invention, the bindingmolecules are present in solution.

These kits may further comprise additional components, such as a buffersolution. Other components may include a probe or labelling molecule forthe detection of the bound protein and so the necessary reagents (i.e.enzyme, buffer, etc) to perform the labelling; binding buffer; washingsolution to remove all the unbound or non-specifically bound miRNAs.Binding of the binding molecules to the target analyte may occur understandard or experimentally determined conditions. The skilled personwould appreciate what stringent conditions are required, depending onthe biomarkers being measured. The stringent conditions may include atemperature high enough to reduce non-specific binding.

The protein arrays used may use fluorescence labelling to determine thepresence and/or concentration of the biomarkers being analysed, althoughother labels can be used (affinity, photochemical or radioisotope tags).Label-free detection methods can also be used, such as surface plasmaresonance (SRR), carbon nanotubes carbon nanowire sensors andmicroelectro-mechanical (MEMS) cantilevers. Near-IR fluorescentdetection may be particularly useful for quantitative detection, inparticular using nitrocellulose coated glass slides.

Quantitative protein analysis using antibody arrays may comprise signalamplification, multicolour detection, and competitive displacementtechniques. Other techniques include scanning electron microscopy forthe analysis of protein chips (SEMPC), which involves countingtarget-coated gold particles that interact specifically with ligands orproteins arrayed on a glass slide by utilizing backscattering electrondetection. Accordingly, methods of the invention may comprise countinginteractions between biomarker protein and their respective specificbindings molecules to achieve a quantitative analysis of the testsample. Quantitative protein detection and analysis is discussed furtherin, for example, Barry & Solovier, “Quantitative protein profiling usingantibody arrays”, Proteomics, 2004, 4(12):3717-3726.

In one embodiment of the invention, there is provided a method ofdiagnosing early-stage pancreatic ductal adenocarcinoma (PDAC)comprising determining the concentration of each of LYVE1, REG1 and TFF1protein in a urine sample and comparing the so determined values to areference. If the concentration of each of the proteins is greater thanthe reference value, early-stage PDAC may be present, and the patientcan be treated accordingly.

Features for the second and subsequent aspects of the invention are asfor the first aspect of the invention mutatis mutandis.

The present invention shall now be further described with reference tothe following examples, which are present for the purposes ofillustration only and are not to be construed as being limiting on theinvention.

EXAMPLES

Clinical Specimens

Healthy, CP and PDAC urine specimens were obtained from the Royal LondonHospital (RLH) for the discovery phase (n=18) and from RLH andUniversity College London (later on jointly referred to as ‘LON’), theDepartment of Surgery, Liverpool University (‘LIV’), and the CNIOMadrid, Spain (SPA), (in total 371 urines) for validation purposes.Urine samples for patients with other benign and malignant hepatobiliarypathologies (n=117) were obtained from LIV. All samples were collectedwith full ethical approval from the involved centres, and with informedconsent from all individuals who donated urine samples. The specimens inall participating centres were collected using the same standardoperating procedures: clean-catch, midstream urine was collected, frozenwithin 2 hours of collection and stored at −80° C. until utilized. Ofimportance, all the samples were derived from patients with no historyof renal diseases; dipstick test analysis (Bayer multistix SG 08935414)was also performed to exclude potential bilirubinemia, proteinuria,bacterial contamination and hematuria. Samples were collected beforesurgery or chemotherapeutic treatment. Matching plasma samples tomeasure CA19.9 were available from RLH and LIV.

GeLC-MS/MS Analysis of Urine Proteomes

Six urine samples (three males and three females) for each group (H, CPand PDAC; in total 18 samples) were utilised: H males/females age 45,50, 60/44, 45, 54 years; CP males/females age 46, 48, 51/47, 69, 74years; PDAC males/females age 44, 74, 84/71, 73, 77 years; male PDACstage all IIB/female two IB, one IIA. All urine samples were desaltedand concentrated as described previously (Weeks M E et al. “Analysis ofthe urine proteome in patients with pancreatic ductal adenocarcinoma”,Proteomics Clinical Applications 2008; 2:1047-57). 20 μg of eachpre-processed pool of three samples per group were separated induplicate on 4-12% mini-gels (Invitrogen); female and male urines wereanalysed separately. The gels were stained with Colloidal Coomassie, andeach sample lane cut using a grid into 40 equally sized slices. Gelslices were digested robotically with trypsin and resultant peptidesanalyzed by nano LC/MS/MS using a nanoAcquity (Waters) interfaced to aLTQ Orbitrap XL tandem mass spectrometer (ThermoFisher). Product iondata were searched against the human IPI protein database using Mascot,and subsequently parsed into the Scaffold software (Proteome Software)for collation into non-redundant protein lists. Reversed databasesearching was used to assess false discovery rates, the target proteinFDR being <0.5% per sample. A semi-quantitative assessment of relativeprotein abundance between PDAC, CP and Healthy samples was obtained byusing the spectral counting approach (Liu H et al., “A model for randomsampling and estimation of relative protein abundance in shotgunproteomics”, Analytical Chemistry, 2004; 76:4193-201).

Urine Biomarkers and CA19.9 Measurements

Total protein concentration in urines was determined by Bradford assay(Coomassie Protein Assay Reagent, Pierce). The quantitativedetermination of human LYVE-1 (Cat # SEB049Hu, Uscn Life Science Inc.)and human TFF1 (Cat # ELH-LYVE1-001, RayBiotech, Inc.) was performedaccording to the manufacturer's instructions; human ReG1A levels wereinitially assessed in our laboratory, and afterwards by BioVendorAnalytical Testing Service (BioVendor—Laboratorni medicina a.$).Calibration curves were prepared using purified standards for eachprotein assessed. Curve fitting was accomplished by a four-parameterlogistic regression following the manufacturer's instructions. Thelimits of detection and the coefficient of variation (CV) for each ofthe ELISA assays were as follows: LYVE-1-8.19 pg/ml, intra-assay CV—9%,inter-assay CV—12%, TFF1—0.037 ng/ml, intra-assay CV—9%, inter-assayCV—12%. REG1A—0.094 ng/ml, intra-assay CV—9%, inter-assay CV 20%;REG1B—3.13 pg/ml, intra-assay CV—3.9%, inter-assay CV—2.7%. Urinecreatinine was measured by the Jaffé method using the Roche Cobas 8000system (Roche Diagnostics, Mannheim, Germany) at the ClinicalBiochemistry Laboratory, RLH (London, UK). Levels of Ca19.9 in plasmaand urine were measured at the Clinical Biochemistry Laboratory, RLHusing a Roche Modular E170 instrument according to the routineprotocols.

Tissue Microarrays and Immunohistochemistry (IHC)

The details of the tissue microarray and scoring procedure used inevaluating the expression of the biomarkers was described previously(Ene-Obong A et al., “Activated pancreatic stellate cells sequester CD8+T cells to reduce their infiltration of the juxtatumoral compartment ofpancreatic ductal adenocarcinoma”, Gastroenterology, 2013; 145:1121-32).IHC was performed with anti-REG1A (Abcam, Rabbit polyclonal, ab47099,1:100 dilution), anti-TFF1 (Abcam, Rabbit polyclonal, ab50806, 1:100dilution), and anti-LYVE1 (Acris, Rabbit polyclonal, DP3500PS, 1:100dilution) antibodies using the Ventana Discovery system, according tostandard protocols (sCC1, 1 h incubation).

Statistical Analysis

To identify potential urine biomarkers from the MS data, the statisticalanalysis was performed on the normalized data (based on the sum ofspectral counts/sample) using Arraytrack software(http;edkb.fda.go/webstart/arraytrack) and a t-test. The data werefurther filtered according to both p values and fold change between anytwo sample groups.

The concentrations of the selected proteins (LYVE1, REG1A and TFF1) weresubsequently explored using ELISA assays, and the obtained resultsanalysed using nonparametric Kruskal-Wallis test followed by Dunn's posttest with GrafPadPrism. Correlation between the three biomarkers wasassessed using Pearson's correlation coefficient.

Each individual biomarker and the panel were investigated for theirability to discriminate between PDAC patients (all stages, or earlystages I-II) and control samples (healthy and CP) using ROC analysis andan hold-out approach. For each comparison, 70% of the subjects in thepatient and control datasets were randomly selected for inclusion in thetraining dataset. Logistic regression was then applied. All proteinconcentration data were natural log-transformed and mean-centered priorto regression analysis. In individual biomarker analyses,creatinine-normalised data were used to correct for the urine dilutionfactor; for the panel analysis, the model included the three biomarkers(prior to creatinine normalisation) and was adjusted for creatinine andage (as the median age of PDAC patients was higher than that of healthyand CP individuals, Table 1), i.e. 5-parameter model. Separate modelswere applied to the training datasets for the comparison of PDAC allstages versus healthy, PDAC stages I-II versus healthy, PDAC all stagesversus CP and PDAC stages I-II versus CP. ROC curves were generated foreach of the above regression models; the area under the curve (AUC), andthe sensitivity (SN) and specificity (SP) at the ‘optimal’ cut-point fordiscrimination between groups were obtained. The optimal cut-pointcorresponded to the point closest to the top-left part of the plot inthe ROC plane (coordinates 0,1) with optimal SN and SP according to thefollowing criterion:min((1−sensitivities)²+(1−specificities)²),as calculated by the ‘ci.threshold’ procedure of the R ‘pROC’ package(Robin X et al., “pROC: an open-source package for R and S+ to analyseand compare ROC curves”, BMC Bioinformatics, 2011; 12:77). This approachbeen showed to have good performance in the estimation of the optimalcut-point of a biomarker.

The rest of the subjects (30%) formed independent datasets which wereused for model validation. For the primary analysis (all PDAC versusHealthy), 49 PDAC and 28 healthy samples give more than 90% power todetect a standardized difference of 1.0 (i.e. a difference between PDACand healthy samples of at least one standard deviation) using aone-sided test.

Validation was performed by classifying each sample in the validationdataset according to the logistic regression model developed based onthe training dataset, and comparing this classification with the actualdiagnosis, hence deriving a new ROC curve. The optimal cut-pointscomputed for the training sets were used to derive the SN and SP of thevalidation dataset. Confidence intervals (CI, 95%) for AUCs were derivedbased on DeLong′ asymptotically exact method to evaluate the uncertaintyof an AUC (DeLong E R et al., “Comparing the areas under two or morecorrelated receiver operating characteristic curves: a nonparametricapproach”, Biometrics, 1988; 44:837-45).); SN and SP, 95% CI werederived using non-parametric stratified resampling with the percentilemethod (2,000 bootstrap replicates) as described by Carpenter J &Bithell J., “Bootstrap confidence intervals: when, which, what? Apractical guide for medical statisticians”, Stat Med, 2000;19:1141-64.). AUCs were compared using DeLong's 1-sided test forcorrelated/paired AUCs).

For exploratory analyses, ROC curves were derived for the comparison ofPDAC stage I-IIA versus healthy or CP based on logistic regressionmodelling using all available samples.

ROC curve analyses were performed in R version 2.13.0 (The R Foundationfor Statistical Computing, www.r-project.org/foundation) usingprocedures from the Epi, pROC http://CRAN.R-project.org/package=Epi andROCR (Sing T et al., “ROCR: visualizing classifier performance in R.”,Bioinformatics, 2005; 21:3940-1) packages.

Urine Proteomes

An in-depth proteomics analysis by GeLC/MS/MS of 18 urine specimensderived from PDAC, CP and healthy (H) individuals (6 per group, threemales, three females) (FIG. 6A) was undertaken. This analysis resultedin the identification of around 1,500 (1,198 in male and 1,061 in femaleurine) non-redundant proteins. These proteins originated from allcellular compartments and were mapped using IPA (Ingenuity pathwayanalysis, www.ingenuity.com/) to a number of cellular functions anddiseases, confirming that urine provides a rich source of diverseproteins with respect to their origin and functional roles.

The MS analysis was performed separately on urine samples from male andfemale subjects. Considerable gender-specific differences were noticed:of 997 proteins identified in healthy urine samples, 398 (40%) wereunique to male urines, 118 (12%) were unique to female and 481 (48%)were common to both.

Among around 200 differentially expressed between the three experimentalgroups (PDAC, CP, H) three proteins commonly deregulated in both malesand females: LYVE1, REG1A and TFF1, were selected for further evaluationbased on the statistics (p-values, fold change), interrogation of bothPancreatic Expression Database (PED) (www.pancreasexpression.org/) andadditional literature search to for previous knowledge on the potentialcandidates, and also on the availability of commercial ELISA assays.While REG1 B in the proteomics data appeared to be slightly bettercandidate, only REG1A ELISA assay was available commercially at thetime. However, when REG1 B ELISA became available, a subset of urinesamples was tested and similar results were obtained (see later). Thepresence of the three selected biomarkers as full-size proteins: 35 kDafor LYVE1, 19 kDa for REG1A, and 9 kDa for TFF1 in urine specimens wasconfirmed by Western blot examination (data not shown).

Biomarker Panel in Detecting PDAC

The selected biomarkers were subsequently assessed using ELISA assays on371 urine samples collected from three centres: London and Liverpool, UK, and Madrid, Spain. Demographics and clinical characteristics ofpatients and healthy participants included in the study are shown inTable 1 (overleaf).

TABLE 1 Demographics and clinical characteristics of the healthy andpatient cohorts Normal CP Age Age PDAC Cases range Cases range Cases Agerange Stage/n (n) Gender (Median) (n) Gender (Median) (n) Gender(Median) plasma LON 87 M = 46 28-87 45 M = 32 29-82 60 M = 38 29-82 I =4/4 F = 41 (55) F = 13 (53) F = 22 (64) IIA = 1/1 IIB = 13/13 III =33/30 IV = 6/5 U = 3/3 Plasma 28 M = 16 28-67 19 M = 14 29-74 56 M = 3429-82 (CA19.9) F = 12 (46) F = 5 (54) F = 22 (64) LIV 0 N/A N/A 41 M =25 29-82 91 M = 53 39-83 I = 3/3 F = 16 (51) F = 38 (68) IIA = 8/8 IIB =42/42 III = 38/38 IV = 0/0 U = 0/0 Plasma 0 N/A N/A 31 M = 17 37-73 91 M= 53 39-83 (CA19.9) F = 14 (51) F = 38 (68) SPA 0 N/A N/A 6 M = 4 54-6841 M = 23 43-94 I = 0/NA F = 2 (57) F = 18 (72) II = 0/NA III = 0/NA IV= 0/NA U = 41/NA Plasma 0 N/A N/A 0 N/A N/A 0 N/A N/A (CA19.9) Total 87M = 46 28-87 92 M = 61 29-82 192 M = 114 29-94 I = 7/7 F = 41 (55) F =31 (54) F = 78 (68) IIA = 9/9 IIB = 55/55 III = 71/68 IV = 6/5 U = 44/3Plasma 28 M = 16 28-67 50 M = 31 29-74 147 M = 87 29-83 (CA19.9) F = 12(46) F = 19 (53) F = 60 (67)PDAC Stage I-IV Versus Healthy

The ELISA analysis showed significantly higher urine concentrations ofeach of the candidate biomarkers in the urine of PDAC patients (n=192)when compared to healthy samples (n=87, all with p<0.0001, FIG. 1). Ofnote, REG1B and REG1A ELISA assays produced similar results (FIG. 1).

In PDAC, LYVE1, REG1A and TFF1 were positively correlated with eachother, while in healthy samples, only LYVE1 and REG1A were correlated(FIG. 7). The diagnostic performance of LYVE1, REG1A and TFF1 wasestablished using Receiver Operating Characteristic (ROC) curve analysis(FIG. 2). Their individual performance in discriminating between PDACstage I-IV and healthy urines was assessed first, in a training dataset(70% of the samples, n=143 and n=59, respectively). Individual(creatinine-normalised) urine biomarkers were able to discriminatebetween the two groups with AUC values of 0.851 (95% CI 0.801-0.902) forLYVE1, 0.823 (95% CI 0.766-0.879) for REG1 and 0.686 (95% CI0.606-0.765) for TFF1, with respective SN of 76.9% (95% CI 69.3-83.2),62.2% (95% CI 53.8-69.9) and 72.7% (95% CI 65.0-79.7), and respective SPof 88.1% (95% CI 79.6-96.6), 94.9% (95% CI 88.1-100.0), and 59.3% (95%CI 47.5-71.2) (FIG. 2A, C). The three biomarkers were then combined intoa panel adjusted for creatinine and age (FIG. 2B). The results of thelogistic regression model underlying the ROC analysis in the trainingand validation (30% of the samples, PDAC n=49, healthy n=28) datasetsare shown in FIGS. 2B and C. The panel achieved SN >75% and SP >85% forAUCs of 0.891 (95% CI 0.847-0.935) and 0.921 (95% CI 0.863-0.978) in thetraining and validation datasets, respectively, thus showing betterperformance than any of the individual biomarkers.

PDAC Early Stages Versus Healthy

Next, the performance of the biomarkers in discriminating early stagecancers from healthy individuals was assessed. Tumour staginginformation was available for 148 (77%) of the PDAC patients. Theconcentrations of each of the biomarkers were significantly increased inlater stages (stage III-IV, n=77, all p<0.001), in stages I-II (n=71,all p<0.001) and in stages I-IIA (locally invasive disease without lymphnode metastases, n=16, p<0.05) compared to healthy people (n=87) (FIG.3). The concentrations of LYVE1 and TFF1 were also higher in stage Icancers (p<0.001 and p<0.05, respectively; data not shown). As a limitednumber of stage I urine samples was available (n=7), the diagnosticaccuracy of the urine markers on combined PDAC stage I-II data wasassessed. The performance of the individual markers and the panel indiscriminating between PDAC stage I-II from healthy urines was firstassessed in a new training dataset (70% of the samples; PDAC stage I-IIn=56 and healthy n=61, respectively). A new 5-parameter model was builtusing this training dataset and validated using the rest of the data(30% of the samples; PDAC stage I-II n=15, Healthy n=26) (FIG. 4A, B).The panel achieved AUCs of 0.900 (95% CI 0.843-0.957) and 0.926 (95% CI0.843-1.000) in the training and validation datasets, respectively (FIG.4C). Therefore, the urine biomarker panel can differentiate early PDACfrom healthy samples with high accuracy.

As an exploratory analysis, the urine samples from individuals for whichmatched plasma samples were available were selected so CA19.9 valuescould be obtained. The ROC curves were derived for plasma CA19.9 (as acategorical variable with a cut-off at clinically established thresholdof 37 U/mL), the panel, and a combination of the panel and CA19.9. Forthe comparison of PDAC stage I-II (n=71) versus healthy (n=28) samples,AUCs of 0.880 (95% CI 0.947-0.999) for CA19.9, and 0.973 (95% CI0.947-0.999) were obtained for the panel, which was significantlygreater than plasma CA19.9 alone (p=0.005). The addition of plasmaCA19.9 to the panel significantly increased the AUC to 0.991 (95% CI0.979-1.000, p=0.04, FIG. 5A/C). When PDAC stage I-IIA (n=16) werecompared to healthy samples, AUCs were 0.839 (95% CI 0.719-0.959) forCA19.9, and 0.971 (95% CI 0.929-1.000) for the panel (p=0.006). Theaddition of plasma CA19.9 to the panel did not result in any improvement(AUC=0.969, 95% CI 0.924-1.000, p=0.7, FIG. 5B/C).

Biomarker Panel in Differentiating PDAC from CP

The ability of the biomarker panel in differentiating PDAC from CP wasassessed.

PDAC Stage I-IV Versus CP

Urine concentration for all three biomarkers was higher in PDAC (n=192)compared to CP samples (n=92), all with p<0.001, (FIG. 1) and as forPDAC, the biomarker concentrations were positively correlated with eachother in the CP data (FIG. 7). In the training dataset (PDAC n=143, CPn=62) LYVE1 and REG1 were able to discriminate between the two groupswith SN of 77-78% and SP of 66-69% (respective AUC values of 0.775 (95%CI 0.704-0.846) and 0.722 (95% CI 0.643-0.801, FIG. 8), while the SP ofTFF1 only reached 50% for a similar SN. Combining the three biomarkersinto a panel only improved marginally the performance of LYVE1 and REG1alone as assessed in the training (AUC=0.815, 95% CI 0.752-0.878), andvalidation (PDAC n=49, CP n=30, AUC=0.839, 95% CI 0.751-0.928) datasets.

PDAC Early Stages Versus CP

Biomarker urine concentrations were significantly increased in stagesI-II PDAC (n=71) compared to CP (n=87), with p<0.001 for each of thethree biomarkers (data not shown). The panel achieved high SN (>85%) inboth the training (PDAC stage I-IIn=56, CP n=66) and validation (PDACstage I-IIn=15, CP n=26) datasets, but relatively low SP (66.7% and50%), similar to the SP observed for individual biomarkers, withrespective AUCs of 0.831 (95% CI 0.762-0.901) and 0.846 (95% CI0.730-0.963, FIGS. 8D-F).

As before, the panel was explored in combination with plasma CA19.9. Forthe comparison of PDAC stage I-II (n=71) versus CP (n=50) samples, theROC curves showed AUCs of 0.775 (95% CI 0.699-0.852) for CA19.9, 0.830(95% CI 0.759-0.902) for the panel (p=0.1), and 0.885 (95% CI0.825-0.945) for the panel in combination with CA19.9 (p=0.01 forsuperiority over the panel alone) (FIGS. 9A/C). In the comparison ofPDAC stage I-IIA (n=16) versus CP, the ROC curves showed AUCs of 0.735(95% CI 0.609-0.861) for CA19.9, 0.871 (95% CI 0.770-0.972) for thepanel (p=0.004 for superiority over plasma CA19.9), and 0.866 (95% CI0.749-0.984) for the combination (p=0.6) (FIG. 9B/C). Therefore, thepanel performed better in differentiating stage I-IIA from CP thanCA19.9.

Biomarker Expression in Urine of Other Hepatobiliary Pathologies

Finally, the expression of the biomarkers in urine specimens collectedfrom patients with several other benign or malignant hepatobiliarypathologies was explored and compared to the expression in patients withearly stage PDAC (FIG. 10). Urine levels of LYVE1 in PDAC stage I-IIsamples were higher than in IPMNs, AMP and pancreatic NETs specimens;REG1A levels were only significantly higher in early stage PDAC comparedto IPMNs. Plasma CA19.9 levels were significantly higher in PDACs stageI-II compared to pancreatic NETs and DuCA samples. This might suggest apotential utility for LYVE1 and REG1A in distinguishing other benign ormalignant hepatobiliary pathologies from early stage PDACs.

Tissue Origin of the Three Biomarkers

Having demonstrated a good performance of the panel in differentiatingearly cancer patients from healthy individuals, it was next sought toestablish the expression of the biomarkers in pancreatic tissue.Immunohistochemistry (IHC) was performed using in-house constructed PDACtissue microarrays. A strong expression of REG1A was seen inhistologically normal adjacent acinar cells, but the staining was alsoseen in 44/60 tumours (73%) (FIG. 11A). TFF1 was absent in normalpancreas, but was expressed in 43/60 (72%) of PDACs (FIG. 11B). While noLYVE1 expression was seen in any of the cancer cells, it was seen inscarce lymphatic vessels in eight PDAC tissues (FIG. 11C). Next, thelevels of all three biomarkers was measured in urines from seven PDACpatients for whom samples were collected prior to and after surgery(FIG. 11D). In all patients, levels of LYVE1 and REG1A decreased aftersurgery, and this was also seen in six out of seven patients for TFF1(except for Patient 1, where the first post-surgical urine sample wascollected four months after the procedure), likely due to substantialloss of tumour mass after surgery.

Finally, as several reports have indicated that CA19.9 is also presentin urine and can be used for cancer diagnosis, and was even superior toblood CA19.9 in some cases, Ca19.9 levels in urine samples were measuredand compared them to matched plasma CA19.9. Urine CA19.9 did not proveuseful in differentiating PDAC from CP and healthy urines in this case(data not shown).

PDAC is one of the most challenging cancers to detect; the majority ofpatients thus present at an advanced stage of the disease. Hence lessthan 20% of PDAC patients undergo potentially curative surgery, whilethe remainder can only be offered palliative treatment. Here, athree-biomarker urine panel is described that discriminates early stagePDAC patients from healthy subjects with high accuracy. A diagnostictest based on urine specimens was developed as this body fluid hasseveral advantages over blood: it is far less complex, provides an‘inert’ and stable matrix for analysis, and can be repeatedly andnon-invasively sampled in sufficient volumes. So far, more than 2,300proteins have been detected in urine, of which at least a third are of asystemic origin. As an ultrafiltrate of blood, it can be expected thatat least some of the biomarkers will be found in higher concentration inurine than in blood.

When combined, REG1A and TFF1, LYVE1 form a powerful urinary panel thatcan detect patients with stages I-II PDAC, with over 90% accuracy. Theexploratory analyses suggest that when combined with CA19.9, accuracymay be increased. In addition, the panel may prove useful indiscriminating patient in stages I-IIA from healthy ones.

Being completely non-invasive and inexpensive, this urine screening testcould, when coupled with timely surgical intervention, lead to a muchimproved outcome in patients with high-risk of developing pancreaticadenocarcinoma.

The invention claimed is:
 1. A kit for testing for pancreatic ductaladenocarcinoma comprising an enzyme-linked immunosorbent assay (ELISA)for measuring the expression or concentration of Lymphatic vesselendothelial hyaluronan receptor 1 (LYVE1), Regenerating islet-derived 1(REG1) and Trefoil factor 1 (TFF1), wherein the REG1 protein isregenerating islet-derived 1A (REG1A) and/or regenerating islet-derived1B (REG1B).
 2. The kit according to claim 1, wherein the kit furthercomprises one or more vessels for containing the biological sample. 3.The kit according to claim 1, wherein the kit further comprises one ormore solvents for extracting LYVE1, REG1, and/or TFF1 proteins from thebiological sample.
 4. The kit of claim 1, wherein the ELISA comprises anantibody that specifically binds to LYVE1, an antibody that specificallybinds to REG1 and an antibody that specifically binds to TFF1.
 5. Thekit of claim 1, wherein the ELISA further comprises an antibody thatspecifically binds to creatinine.
 6. The kit of claim 1, wherein theELISA comprises at least three separate ELISAs, a first ELISA comprisingan antibody that specifically binds to LYVE1, second ELISA comprising anantibody that specifically binds to REG1 and a third ELISA comprising anantibody that specifically binds to TFF1.
 7. The kit of claim 6, whereinthe ELISA further comprises a fourth separate ELISA comprising anantibody or antibody fragment capable of binding that specifically bindsto creatinine.
 8. The kit of claim 4, wherein the antibody thatspecifically binds to REG 1 comprises an antibody that specificallybinds to REG1A and/or antibody that specifically binds to REG1B.
 9. Thekit of claim 4, wherein the antibody that specifically binds to REG 1comprises an antibody that specifically binds to REG1B.
 10. The kit ofclaim 4, wherein the antibodies are immobilized on a solid surface. 11.The kit of claim 4, wherein the antibodies are immobilized on amicrotiter plate.
 12. A combination, comprising the kit of claim 1 and aurine sample.
 13. The kit of claim 1, wherein the ELISA is a multiplexELISA.
 14. A method of testing for pancreatic ductal adenocarcinoma(PDAC) comprising determining the level of expression or concentrationof each of Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1),Regenerating islet-derived 1 (REG1) and Trefoil factor 1 (TFF1), whereinthe REG1 protein is regenerating islet-derived 1A (REG1A) and/orregenerating islet-derived 1B (REG1B) by contacting a urine sample tothe assay of claim
 1. 15. The method according to claim 14, wherein thesample is from a human subject having or suspected of having PDAC. 16.The method according to claim 14, wherein the pancreatic ductaladenocarcinoma is stage I or stage II pancreatic ductal adenocarcinoma.17. The method of claim 14, wherein the method provides differentialdiagnosis distinguishing between stage I PDAC and a healthy patient,between stage I PDAC and chronic pancreatitis (CP), or between stage IPDAC and a later stage of PDAC.
 18. The method of claim 14, furthercomprising the step of comparing the level of expression orconcentration of one or more miRNAs with a reference.
 19. The method ofclaim 18, wherein the reference is a biological sample from a healthypatient.
 20. The method of claim 14, further comprising determining thelevel of expression or concentration of CA19.9 protein.